Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

Abstract

The study investigates Natural Language Processing (NLP) applications for clinical cancer phenotype extraction from Electronic Medical Records (EMRs) which serve a critical purpose in medical diagnosis improvement and treatment design. The unstructured textual information in EMR systems includes clinical notes and radiology reports and pathology results alongside other clinical documentation which proves difficult to work through manually. Organizing data through NLP methods transforms unstructured clinical information into usable resources that benefit both clinical care support systems and cancer research activities. This analysis covers modern approaches in NLP for cancer information detection alongside challenges faced and how machine learning developments enhanced extraction precision. The discourse confronts obstacles including medical data protection regulations as well as complexities in medical terminology together with data collection requirements for substantial datasets. The paper demonstrates NLP's potential to transform cancer care through enhanced treatment efficiency alongside precision and personalized approaches in treatment while identifying technical and ethical challenges for resolution